Covid-19 Response Stringency (1)
Andriy Koval
May 7, 2020
Abstract
Explores the response of OECD countries
Environment
library(ggplot2) #For graphing
library(magrittr) #Pipes
library(dplyr) # for shorter function names. but still prefer dplyr:: stems
library(knitr) # dynamic documents
library(rmarkdown) # dynamic
library(kableExtra) # enhanced tables, see http://haozhu233.github.io/kableExtra/awesome_table_in_html.html
# library(TabularManifest) # exploratory data analysis, see https://github.com/Melinae/TabularManifest
requireNamespace("knitr", quietly=TRUE)
requireNamespace("scales", quietly=TRUE) #For formating values in graphs
requireNamespace("RColorBrewer", quietly=TRUE)
requireNamespace("dplyr", quietly=TRUE)
requireNamespace("DT", quietly=TRUE) # for dynamic tables
# requireNamespace("plyr", quietly=TRUE)
# requireNamespace("reshape2", quietly=TRUE) #For converting wide to long
# requireNamespace("mgcv, quietly=TRUE) #For the Generalized Additive Model that smooths the longitudinal graphs.config <- config::get()
source("./scripts/common-functions.R") # reporting functions and quick views
# source("./scripts/graphing/graph-presets.R") # font and color conventions
# source("./scripts/graphing/graph-support.R") # font and color conventionsggplot2::theme_set(ggplot2::theme_bw())compute_epi_timeline <- function(d, n_deaths_first_day = 1) { #}, d_country ){
# browser()
# d <- ds_cgrt %>% filter(country_code %in% c("ITA","FRA") ) %>%
# select(country_code, date, n_cases, n_deaths)
#
d_out <- d %>%
# dplyr::filter(country_code %in% unique(d_country$id)) %>%
dplyr::group_by(country_code) %>%
dplyr::mutate(
# this solution might be vulnerable to cases where some intermediate dates are missed
n_deaths_cum = cumsum(tidyr::replace_na(n_deaths,0))
,n_cases_cum = cumsum(tidyr::replace_na(n_cases,0))
,cutoff_death = n_deaths_cum >= 1
,cutoff_case = n_cases_cum >= 1
,days_since_1death = cumsum(tidyr::replace_na(cutoff_death,0))
,days_since_1case = cumsum(tidyr::replace_na(cutoff_case,0))
) %>%
dplyr::ungroup() %>%
# dplyr::filter(epi_timeline > 0) %>%
dplyr::mutate(
days_since_exodus = date - lubridate::date("2020-01-13")
) %>%
select(-cutoff_death, - cutoff_case)
return(d_out)
}Data
The data comes from [OxCGRT][] and [OECD][] databases
# list of focal countries in OECD database
ds_country <-
readr::read_csv(
config$path_country
) %>%
dplyr::filter(desired)
# ECDC
# path_save <- paste0("./data-unshared/derived/ocdc-",Sys.Date(),".csv")
ds_covid <- readr::read_csv(config$path_input_covid)
# ds_covid %>% glimpse()
# OECD
file_path <- list.files(config$path_oecd_clean,full.names = T,recursive = T,pattern = ".rds$")
dto <- list()
for(i in seq_along(file_path)){
file_name <- basename(file_path[i]) %>% stringr::str_replace(".rds","")
dto[[file_name]] <- readr::read_rds(file_path[i])
}
# str(dto,max.level = 1)
ls_health_resources <- dto$health_resources
# ls_health_resources %>% str(1)
ds_hr <- ls_health_resources$data_agg
# ds_hr %>% glimpse()
# OxCGRT
ds_cgrt <- readr::read_rds("./data-unshared/derived/OxCGRT.rds")
# ds_cgrt %>% glimpse()
# n_distinct(ds_cgrt$country_name)Data Tweaks
ds0 <- ds_cgrt %>%
dplyr::mutate(
n_deaths = tidyr::replace_na(n_deaths, 0)
,n_cases = tidyr::replace_na(n_cases, 0)
) %>%
# select(country_code, country_name, date, ConfirmedCases, ConfirmedDeaths,n_cases, n_deaths, StringencyIndex) %>%
# select(country_code, date, ConfirmedCases, ConfirmedDeaths,n_cases, n_deaths ) %>%
# filter(country_code == "AFG") %>%
compute_epi_timeline() %>%
group_by(country_code) %>%
mutate(
n_deaths_cum_per_1m = n_deaths_cum/n_population_2018*1000000
,n_cases_cum_per_1m = n_cases_cum/n_population_2018*1000000
) %>%
ungroup() %>%
select(
country_code, date, days_since_1death, days_since_1case, days_since_exodus,
n_cases, n_deaths, n_deaths_cum, ConfirmedDeaths,n_cases_cum, ConfirmedCases,
geo_id, country_name, continent,
dplyr::everything()
)
# n_distinct(ds0$country_code)
# test the logic
# d_out <- ds0 %>% filter(country_code == "ITA") %>%
# select(
# country_code, date, days_since_1death, days_since_1case, days_since_exodus,
# n_cases, n_deaths, n_deaths_cum, n_cases_cum,
# geo_id, country_name,
# StringencyIndex,
# H1_Public_information_campaigns, H2_Testing_policy, H3_Contact_tracing,
# H4_Emergency_investment_in_healthcare, H5_Investment_in_vaccines
# )Q1 - Trend of Response Magnitute
What was the trend of the response to COVID-10 by each country?
# What the trend response to COVID-10 by each country?
d1 <- ds0 %>%
filter(country_code %in% ds_country$id)
g1 <- d1 %>%
ggplot(aes(x = days_since_exodus, y = StringencyIndex))+
geom_line()+
geom_point(data = d2 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d2 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
facet_wrap(~country_name)+
labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Stringency Index"
,y = "Stringency Index", x = "Days since first case outside of China (Jan 13, 2020)"
, caption = "First dot = 1st confired case, Second dot = 1st confirmed death"
)
g1
Q2
d2 <- ds0 %>%
filter(country_code %in% ds_country$id)
g2 <- d2 %>%
filter(country_code %in% ds_country$id) %>%
# filter(country_code == "ITA") %>%
ggplot(aes(x = days_since_exodus, y = StringencyIndex, group = country_name))+
geom_line(aes(color = continent), alpha = .4)+
geom_point(data = d2 %>% filter(days_since_1case == 1), size = 2, fill = "#1b9e77", color = "black", alpha = .5, shape = 21)+
geom_point(data = d2 %>% filter(days_since_1death == 1), size = 2, fill = "#d95f02", color = "black", alpha = .5, shape = 21)+
labs(
title = "Timeline of OECD countries' respones to COVID-19 as measured by the Stringency Index"
,y = "Stringency Index", x = "Days since first case outside of China (Jan 13, 2020)"
, caption = "First dot = 1st confired case, Second dot = 1st confirmed death"
)
g2 <- plotly::ggplotly(g2)
g2
session information
For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.
Environment
- Session info -------------------------------------------------------------------------------------------------------
setting value
version R version 3.6.3 (2020-02-29)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.1252
ctype English_United States.1252
tz America/New_York
date 2020-05-07
- Packages -----------------------------------------------------------------------------------------------------------
package * version date lib source
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[1] C:/Users/an499583/Documents/R/win-library/3.6
[2] C:/Users/an499583/Documents/R/R-3.6.3/library